15,225 research outputs found
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
Non-Redundant Spectral Dimensionality Reduction
Spectral dimensionality reduction algorithms are widely used in numerous
domains, including for recognition, segmentation, tracking and visualization.
However, despite their popularity, these algorithms suffer from a major
limitation known as the "repeated Eigen-directions" phenomenon. That is, many
of the embedding coordinates they produce typically capture the same direction
along the data manifold. This leads to redundant and inefficient
representations that do not reveal the true intrinsic dimensionality of the
data. In this paper, we propose a general method for avoiding redundancy in
spectral algorithms. Our approach relies on replacing the orthogonality
constraints underlying those methods by unpredictability constraints.
Specifically, we require that each embedding coordinate be unpredictable (in
the statistical sense) from all previous ones. We prove that these constraints
necessarily prevent redundancy, and provide a simple technique to incorporate
them into existing methods. As we illustrate on challenging high-dimensional
scenarios, our approach produces significantly more informative and compact
representations, which improve visualization and classification tasks
Improving Landmark Localization with Semi-Supervised Learning
We present two techniques to improve landmark localization in images from
partially annotated datasets. Our primary goal is to leverage the common
situation where precise landmark locations are only provided for a small data
subset, but where class labels for classification or regression tasks related
to the landmarks are more abundantly available. First, we propose the framework
of sequential multitasking and explore it here through an architecture for
landmark localization where training with class labels acts as an auxiliary
signal to guide the landmark localization on unlabeled data. A key aspect of
our approach is that errors can be backpropagated through a complete landmark
localization model. Second, we propose and explore an unsupervised learning
technique for landmark localization based on having a model predict equivariant
landmarks with respect to transformations applied to the image. We show that
these techniques, improve landmark prediction considerably and can learn
effective detectors even when only a small fraction of the dataset has landmark
labels. We present results on two toy datasets and four real datasets, with
hands and faces, and report new state-of-the-art on two datasets in the wild,
e.g. with only 5\% of labeled images we outperform previous state-of-the-art
trained on the AFLW dataset.Comment: Published as a conference paper in CVPR 201
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